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False forest fire hotspot filtering method based on dbscan algorithm

A filtering method and forest fire technology, applied in computing, computer parts, data processing applications, etc., can solve the problems of time-consuming and laborious, accuracy not reaching a perfect value, historical data utilization not being paid attention to, etc., to improve accuracy Effect

Active Publication Date: 2022-05-27
CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY
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Problems solved by technology

The use of historical data has not been valued
(4) Although the research on spatio-temporal data mining has been developed to a considerable extent in recent years, in the practical application of spatio-temporal data mining, sometimes it is time-consuming and laborious, and the accuracy does not reach a perfect value, so that the application of the model lacks a considerable theoretical background

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  • False forest fire hotspot filtering method based on dbscan algorithm
  • False forest fire hotspot filtering method based on dbscan algorithm
  • False forest fire hotspot filtering method based on dbscan algorithm

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Embodiment Construction

[0036] By analyzing the historical forest fire hotspot data automatically interpreted by the computer in the database, it is found that the historical forest fire hotspot data has the characteristics of time series distribution, geographical distribution characteristics and spatial distribution characteristics. These three features meet the requirements of spatiotemporal data mining, and also have spatiotemporal clustering features. Therefore, the spatiotemporal clustering rules are used to mine false forest fire hotspots.

[0037] The spatio-temporal clustering rule refers to classifying these spatio-temporal objects according to one or more attributes of spatio-temporal objects through some similar or similar principle, so that spatio-temporal objects with similar or similar attributes form a cluster. At the same time, dissimilar spatiotemporal objects are separated from clustered spatiotemporal objects to form distinct classifications.

[0038] This application studies a m...

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Abstract

The invention relates to a method for filtering false forest fire hotspots based on the DBSCAN algorithm, comprising: determining parameters of spatio-temporal clustering; through the determined parameters, using the DBSCAN algorithm to analyze historical forest fire hotspot data; based on the above analysis, extracting false forest fires Hot hot. The method for identifying false forest fire hotspots based on spatio-temporal data of the present invention can quickly eliminate false forest fire hotspots caused by fixed heat sources on remote sensing images through the fixed heat source database formed by mining historical forest fire hotspot data interpreted by computers .

Description

[0001] Technology neighborhood [0002] The present invention relates to a false forest fire hot spot filtering method based on DBSCAN algorithm. Background technique [0003] Forest fires not only cause economic losses but also seriously endanger forests and forest ecosystems. Improving the monitoring of forest fire hotspots is of great significance to the protection of forest resources. Remote sensing satellites have the characteristics of wide monitoring coverage, high spatial and temporal resolution, and convenient data acquisition, which play an important role in monitoring forest fire hotspots. However, when monitoring forest fire hot spots through remote sensing satellites, all hot spots on the ground will be extracted, which seriously affects the accuracy of forest fire monitoring. Therefore, how to remove the false forest fire hot spots from the satellite remote sensing forest fire hot spot monitoring data is the key to improve the accuracy of forest fire monitoring....

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06Q50/26
CPCG06Q50/26G06F18/23G06F18/22
Inventor 张贵蔡琼吴鑫谭三清
Owner CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY
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